deep_climate_emulator

A Deep Neural Network approach for estimating precipitation fields in Earth System Models.

To install package

pip install git+https://github.com/hutchresearch/deep_climate_emulator

Training models

We provide trainer.py as an example script that utilizes our residual network training package. To train a model using the same architecture and hyperparameters used in our 18-Layer Residual Network, run the following:

# Pull scripts to local machine, if not already available.
git clone https://github.com/hutchresearch/deep_climate_emulator 

# Install the "deep_climate_emulator" package, if not already installed.
pip install git+https://github.com/hutchresearch/deep_climate_emulator

# Navigate to "scripts" directory and run "trainer.py".
cd deep_climate_emulator/scripts
python trainer.py \
--data <PATH_TO_PRECIPITATION_DATA> \
--architecture ../configs/18-layer-ResNet_architecture.json \
--hyperparameters ../configs/18-layer-ResNet_hyperparameters.json

Note: Our package was developed using Python 3. Python 2 compatibility is not guaranteed.

Using pretrained models to generate precipitation forecasts

We provide inference.py as an example script for generating predictions with a pretrained model. To generate predictions with our 18-Layer Residual Network run the following:

# Pull scripts to local machine, if not already available.
git clone https://github.com/hutchresearch/deep_climate_emulator 

# Install the "deep_climate_emulator" package, if not already installed.
pip install git+https://github.com/hutchresearch/deep_climate_emulator

# Navigate to "scripts" directory and run "inference.py".
cd deep_climate_emulator/scripts
python inference.py \
--data <PATH_TO_PRECIPITATION_DATA> \
--window_size 60 \
--num_forecasts 120 \
--num_preforecasts 30 \
--outfile <OUTPUT_FILENAME>

This package comes bundled with a pretrained 18-layer Residual Network (window size = 60), and this will be loaded by default if TensorFlow checkpoint files are not provided with the --model flag.

GPU support

It is highly recommended to train your models with a GPU! To train using a GPU, make sure you have the appropriate versions of TensorFlow and cuDNN installed on your system. For more information on GPU support see: https://www.tensorflow.org/install/gpu

Inference using pretrained models, on the other hand, will run sufficiently fast on CPUs alone (i.e. without GPU support). Our package uses a version of TensorFlow that runs on CPUs by default.

Training Data

All climate model output used in this study as training data is available through the Earth System Grid Federation.

Citations